





from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy



@add_start_docstrings(
    "The RoBERTa Model transformer with early exiting (DeeRoBERTa). "       ,				__SCREAMING_SNAKE_CASE       ,				)
class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     RobertaConfig
 lowercase_						=     "roberta"

 def __init__(self							:  List[Any]			,					UpperCAmelCase_							:  Union[str, Any])			->Union[str, Any]:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_)

   lowerCamelCase__: Any         =RobertaEmbeddings(UpperCAmelCase_)
   self.init_weights()



@add_start_docstrings(
    "RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. "       ,				__SCREAMING_SNAKE_CASE       ,				)
class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     RobertaConfig
 lowercase_						=     "roberta"

 def __init__(self							:  Optional[Any]			,					UpperCAmelCase_							:  List[str])			->str:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_)
   lowerCamelCase__: Union[str, Any]         =config.num_labels
   lowerCamelCase__: Union[str, Any]         =config.num_hidden_layers

   lowerCamelCase__: Dict         =DeeRobertaModel(UpperCAmelCase_)
   lowerCamelCase__: Optional[Any]         =nn.Dropout(config.hidden_dropout_prob)
   lowerCamelCase__: Tuple         =nn.Linear(config.hidden_size			,					self.config.num_labels)







 @add_start_docstrings_to_model_forward(UpperCAmelCase_)
 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  Dict=None			,					UpperCAmelCase_							:  Tuple=None			,					UpperCAmelCase_							:  Dict=None			,					UpperCAmelCase_							:  Optional[int]=None			,					UpperCAmelCase_							:  Optional[int]=None			,					UpperCAmelCase_							:  int=None			,					UpperCAmelCase_							:  Any=None			,					UpperCAmelCase_							:  Dict=-1			,					UpperCAmelCase_							:  List[Any]=False			,					)			->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =self.num_layers
   try:
     lowerCamelCase__: Any         =self.roberta(
         UpperCAmelCase_			,					attention_mask=UpperCAmelCase_			,					token_type_ids=UpperCAmelCase_			,					position_ids=UpperCAmelCase_			,					head_mask=UpperCAmelCase_			,					inputs_embeds=UpperCAmelCase_			,					)

     lowerCamelCase__: int         =outputs[1]

     lowerCamelCase__: int         =self.dropout(UpperCAmelCase_)
     lowerCamelCase__: Optional[Any]         =self.classifier(UpperCAmelCase_)
     lowerCamelCase__: List[Any]         =(logits,) + outputs[2:]  # add hidden states and attention if they are here
   except HighwayException as e:
     lowerCamelCase__: List[str]         =e.message
     lowerCamelCase__: str         =e.exit_layer
     lowerCamelCase__: Dict         =outputs[0]

   if not self.training:
     lowerCamelCase__: Dict         =entropy(UpperCAmelCase_)
     lowerCamelCase__: Tuple         =[]
     lowerCamelCase__: Optional[int]         =[]
   if labels is not None:
     if self.num_labels == 1:
       #  We are doing regression
       lowerCamelCase__: Optional[int]         =MSELoss()
       lowerCamelCase__: Tuple         =loss_fct(logits.view(-1)			,					labels.view(-1))
     else:
       lowerCamelCase__: str         =CrossEntropyLoss()
       lowerCamelCase__: Optional[int]         =loss_fct(logits.view(-1			,					self.num_labels)			,					labels.view(-1))

     # work with highway exits
     lowerCamelCase__: List[Any]         =[]
     for highway_exit in outputs[-1]:
       lowerCamelCase__: List[Any]         =highway_exit[0]
       if not self.training:
         highway_logits_all.append(UpperCAmelCase_)
         highway_entropy.append(highway_exit[2])
       if self.num_labels == 1:
         #  We are doing regression
         lowerCamelCase__: Optional[Any]         =MSELoss()
         lowerCamelCase__: Union[str, Any]         =loss_fct(highway_logits.view(-1)			,					labels.view(-1))
       else:
         lowerCamelCase__: List[str]         =CrossEntropyLoss()
         lowerCamelCase__: List[str]         =loss_fct(highway_logits.view(-1			,					self.num_labels)			,					labels.view(-1))
       highway_losses.append(UpperCAmelCase_)

     if train_highway:
       lowerCamelCase__: Optional[Any]         =(sum(highway_losses[:-1]),) + outputs
       # exclude the final highway, of course
     else:
       lowerCamelCase__: Any         =(loss,) + outputs
   if not self.training:
     lowerCamelCase__: Any         =outputs + ((original_entropy, highway_entropy), exit_layer)
     if output_layer >= 0:
       lowerCamelCase__: Tuple         =(
           (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
       )  # use the highway of the last layer

   return outputs  # (loss), logits, (hidden_states), (attentions), entropy







'''simple docstring'''


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class  __lowercase    (	__magic_name__							,				unittest.TestCase			):
   _a    						=   PriorTransformer
   _a    						=   """hidden_states"""







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       floats_tensor((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       floats_tensor((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=0   )					->     Union[str, Any]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       4
     __a								=       8
     __a								=       7

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   @property
   def 		UpperCamelCase__      (						self   )					->     List[str]:
     return (4, 8)







   @property
   def 		UpperCamelCase__      (						self   )					->     str:
     return (4, 8)







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       {
         'num_attention_heads': 2,
         'attention_head_dim': 4,
         'num_layers': 2,
         'embedding_dim': 8,
         'num_embeddings': 7,
         'additional_embeddings': 4,
     }
     __a								=       self.dummy_input
     return init_dict, inputs_dict







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a						,							__a								=       PriorTransformer.from_pretrained(
         'hf-internal-testing/prior-dummy'   ,				output_loading_info=UpperCamelCase   )
     self.assertIsNotNone(UpperCamelCase   )
     self.assertEqual(len(loading_info['missing_keys']   )   ,				0   )

     model.to(UpperCamelCase   )
     __a								=       model(**self.dummy_input   )[0]

     assert hidden_states is not None, "Make sure output is not None"







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a						,							__a								=       self.prepare_init_args_and_inputs_for_common()

     __a								=       self.model_class(**UpperCamelCase   )
     __a								=       inspect.signature(model.forward   )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __a								=       [*signature.parameters.keys()]

     __a								=       ['hidden_states', 'timestep']
     self.assertListEqual(arg_names[:2]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'   )
     __a								=       model.to(UpperCamelCase   )

     if hasattr(UpperCamelCase   ,				'set_default_attn_processor'   ):
       model.set_default_attn_processor()

     __a								=       self.get_dummy_seed_input()

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     __a								=       output[0, :5].flatten().cpu()
     print(UpperCamelCase   )

     # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
     # the expected output slices are not the same for CPU and GPU.
     __a								=       torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]   )
     self.assertTrue(torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				rtol=1e-2   )   )





@slow
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=1   ,				UpperCamelCase=768   ,				UpperCamelCase=77   ,				UpperCamelCase=0   )					->     List[str]:
     torch.manual_seed(UpperCamelCase   )
     __a								=       batch_size
     __a								=       embedding_dim
     __a								=       num_embeddings

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )

     __a								=       torch.randn((batch_size, embedding_dim)   ).to(UpperCamelCase   )
     __a								=       torch.randn((batch_size, num_embeddings, embedding_dim)   ).to(UpperCamelCase   )

     return {
         "hidden_states": hidden_states,
         "timestep": 2,
         "proj_embedding": proj_embedding,
         "encoder_hidden_states": encoder_hidden_states,
     }







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
           [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
           # fmt: on
       ]   )
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'   ,				subfolder='prior'   )
     model.to(UpperCamelCase   )
     __a								=       self.get_dummy_seed_input(seed=UpperCamelCase   )

     with torch.no_grad():
       __a								=       model(**UpperCamelCase   )[0]

     assert list(sample.shape   ) == [1, 768]

     __a								=       sample[0, :8].flatten().cpu()
     print(UpperCamelCase   )
     __a								=       torch.tensor(UpperCamelCase   )

     assert torch_all_close(UpperCamelCase   ,				UpperCamelCase   ,				atol=1e-3   )



